As is known in the art, machine intelligence (MI)—or machine learning (ML)—enables computers to learn without being explicitly programmed. MI focuses on algorithms that can learn from, and make predictions on, data.
Artificial neural networks (commonly referred to as just “neural networks”) are computing systems inspired by the biological neural networks found in animal brains. Neural networks can learn tasks by considering examples, typically reducing the extent of task-specific programming. A neural network is based on a collection of connected nodes (or “artificial neurons”). Each connection between nodes can transmit a signal from one to another. The node that receives the signal can process it and then signal other nodes connected to it.
More recently, a branch of MI called “deep learning” has been deployed to achieve higher accuracy for many different tasks, including speech recognition, image recognition, and video analytics. A deep learning neural network may have millions or even billions of highly connected nodes. Although deep learning can provide higher accuracy compared to traditional MI techniques, this higher accuracy comes at the cost of higher compute requirements during training. While cloud-based computing environments make it possible to allocate a large number of compute nodes to a given problem, effectively scaling computing resources for neural networks has typically required human intervention.